A novel deep network with triangular-star spatial–spectral fusion encoding and entropy-aware double decoding for coronary artery segmentation

Coronary artery segmentation is a crucial prerequisite for computer-aided diagnosis of coronary artery disease (CAD). However, this task remains challenging due to the intricate anatomical structures and morphologies of coronary arteries (CAs), which are characterized by tortuous and numerous slender branches, large inter-subject variations, and low contrast with adjacent tissues. To address these challenges, we propose a novel deep network with triangular-star spatial–spectral fusion encoding and an entropy-aware double decoding process to comprehensively explore CA features from diverse perspectives. Specifically, to enhance the encoder's ability to exploit spectral characteristics, we incorporate a tri-stage attention-mediated Fourier (tri-AMF) structure. This structure dynamically modulates the global features of blood vessels in the frequency domain with superior resilience against spectral noises. Simultaneously, we introduce a triangular-star cross-domain feature fusion (▽-Star fusion) module, integrating features from a pair of closely intertwined encoders dedicated to spatial and spectral domains, along with features transmitted to the decoder. This module, facilitated by richly connected pairwise interaction pathways, is designed to learn to segment coronary arteries through cross-domain deep analysis. Furthermore, our network's decoder employs a novel local entropy-aware double decoding (LEAD2) process to adaptively fuse feature maps across all scales with the local entropy associated with each scale, explicitly modeling the network's derivation of the final segmentation outcome. Extensive experiments on two in-house and three publicly available datasets consistently demonstrate that the proposed method has superior performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. The code is available at https://github.com/Cassie-CV/CASeg.